Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

Liu, Feng; Zhu, Jun; Lv, Baolong; Yang, Lei; Sun, Wenli; Dai, Zhehao; Gou, Fangfang; Wu, Jia

doi:10.1155/2022/9990092

Cited by 19 publications

(18 citation statements)

References 71 publications

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“…The data for this article are from the Research Center for Artificial Intelligence of Monash University [ 51 ]. The original dataset included more than 4000 MRI image samples from 204 patients with osteosarcoma.…”

Section: Experimental Analysismentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

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Section: Experimental Analysismentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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“…Various devices in a city communicate with each other through the Internet. Thus, mobile device users and mobile devices in the Internet can be abstracted into users and nodes in the community [40,41]. With the continuous increase in the number of mobile device of users and the exponential increase in the amount of data generated, data redundancy will be caused when PB (1024 TB) level data communication is performed in the context of 5G due to the limitation of node cache size and data processing capability [42][43][44][45][46].…”

Section: System Model Designmentioning

confidence: 99%

An effective data communication community establishment scheme in opportunistic networks

Hao

Gou

2023

IET Communications

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The network transmission speed has been greatly improved, thanks to the power of 5G technology. The millisecond‐level communication delay has made a qualitative leap in communication quality. However, the sharp increase in the number of nodes connected to the internet has resulted in an explosion of traffic. Ensuring stable network transmission in the face of large data volumes has become an urgent problem to be solved. Existing research mainly optimizes for low data volumes of nodes, and cannot dynamically adapt to node transmission load to cope with explosive growth. This is because the node's cache is always at a high level, which causes community communication to be blocked. To address this issue, the authors have designed an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user and using the dynamic feedback adjustment (DFA) mechanism, the authors can combine the entire communication community and control the user's data information flow concentration at a relatively low level. This degree of optimization ensures that the user node maintains a relatively stable state for fast communication. The authors design an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user, using the dynamic feedback adjustment mechanism and combining the entire communication community, the user's data information flow concentration is controlled at a relatively low level.

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“…Developing countries exhibit a serious disparity between doctors and patients and uneven medical development, as evidenced by surveys [49][50][51]. Compared with other diseases, the diagnosis and treatment cycle of osteosarcoma is long, requiring more time and cost.…”

Section: System Model Designmentioning

confidence: 99%

Image segmentation technology based on transformer in medical decision‐making system

Gou

2023

IET Image Processing

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Due to the improvement in computing power and the development of computer technology, deep learning has pene‐trated into various fields of the medical industry. Segmenting lesion areas in medical scans can help clinicians make accurate diagnoses. In particular, convolutional neural networks (CNNs) are a dominant tool in computer vision tasks. They can accurately locate and classify lesion areas. However, due to their inherent inductive bias, CNNs may lack an understanding of long‐term dependencies in medical images, leading to less accurate grasping of details in the images. To address this problem, we explored a Transformer‐based solution and studied its feasibility in medical imaging tasks (OstT). First, we performed super‐resolution reconstruction on the original MRI image of osteosarcoma and improved the texture features of the tissue structure to reduce the error caused by the unclear tissue structure in the image during model training. Then, we propose a Transformer‐based method for medical image segmentation. A gated axial attention model is used, which augments existing architectures by introducing an additional control mechanism in the self‐attention module to improve segmentation accuracy. Experiments on real datasets show that our method outper‐forms existing models such as Unet. It can effectively assist doctors in imaging examinations.

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Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

Cited by 19 publications

References 71 publications

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

An effective data communication community establishment scheme in opportunistic networks

Image segmentation technology based on transformer in medical decision‐making system

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